Search Results (3,866)

The stable production of crops such as corn, wheat, soybeans, and canola is increasingly threatened by widespread pest infestations. Conventional manual pest surveys are hampered by low operational efficiency, subjective assessment bias, and delayed feedback, thereby impeding their ability to satisfy the demands of precision agriculture. To address these challenges, we proposes an intelligent pest detection framework based on EfficientNet and Feature Pyramid Network (FPN) for fast and accurate field pest identification. EfficientNet is adopted as the lightweight attention-embedded backbone to extract hierarchical features, and multi-scale detection plus hierarchical FPN fusion are integrated to improve recognition performance for tiny, inconspicuous pests. The experimental results on 37 common pest species in field crops showed that the proposed model achieves a mean average precision at Intersection-over-Union (IoU) threshold 0.5 (mAP@0.5) of 98.89%, 1.57% average recognition error rate, and with an average inference time of merely 0.048 s per image, balancing outstanding detection accuracy and real-time performance. Furthermore, this approach delivers a lightweight, reliable, and automated monitoring solution for field pest surveillance, thereby facilitating data-driven, precise pest management and advancing the practice of sustainable, green precision agriculture. Full article

Accurately simulating the spatiotemporal distribution of global atmospheric CO₂ remains challenging yet essential for reducing uncertainties in carbon source-sink inversions, quantifying the climate effects of heterogeneous CO₂ fields, and supporting the development of CO₂ observation networks. In this study, we simulated global atmospheric CO₂ concentrations (2010–2019) at a horizontal spatial resolution of 1° × 1° using the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) without data assimilation, with initial fields and flux data from the CarbonTracker CT2022 (CT2022) reanalysis product. The simulations were comprehensively evaluated against CT2022 and observations from ground-based (NOAA GML), airborne (ObsPack), and satellite (OCO-2) platforms. The results indicate that across all evaluated surface stations, CT2022 exhibits poorer overall statistical performance (R = 0.69, RMSE = 5.37 ppm, MB = 2.05 ppm) primarily due to noticeable overestimations at unassimilated ground stations, while IAP-AACM maintains robust performance across the surface network (R = 0.84, RMSE = 2.62 ppm, MB = 0.28 ppm). Vertically, airborne observations across eight global campaigns confirm that IAP-AACM accurately reproduces the vertical distribution of CO₂, maintaining strong correlations (R = 0.72–1.00) and performance comparable to the CT2022 reanalysis (R = 0.86–1.00). In terms of total column CO₂ concentrations (XCO₂), IAP-AACM exhibits strong agreement with satellite retrievals annually (R = 0.97, RMSE = 1.08 ppm, MB = 0.26 ppm), with seasonal metrics remaining consistently robust across all four seasons (R = 0.96–0.97, RMSE = 0.98–1.20 ppm, MB = 0.13–0.37 ppm), demonstrating large-scale transport fidelity on par with the CT2022 reanalysis. Finally, across representative ObsPack land sites, unassimilated IAP-AACM achieves a high median correlation (R = 0.97), low error (RMSE = 2.01 ppm), and low mean bias (MB = −0.45 ppm), closely approaching the assimilated CT2022 reanalysis product (R = 0.98, RMSE = 1.40 ppm, MB = −0.07 ppm). Further analysis indicates that the optimized IAP-AACM exhibits robust performance under stable boundary layer conditions, where the revised diffusion scheme produces higher vertical diffusion coefficients that help mitigate excessive near-surface CO₂ accumulation during nighttime. Overall, the optimized IAP-AACM effectively simulates the spatiotemporal distribution of global atmospheric CO₂, serving as a reliable tool to support advanced research. Full article

Ocean sound speed profile (SSP) is a key parameter for underwater acoustic detection, remote sensing, and seafloor geodetic positioning, and its temporal prediction is essential for improving acoustic positioning accuracy. Conventional direct measurements are inefficient and spatially sparse, while statistical and acoustic inversion methods fail to capture the strong nonlinear evolution of the sound speed field. Among existing time series models, LSTM, a recurrent network for time series forecasting, lacks an explicit receptive field. In contrast, the original TCN, a temporal convolutional network with dilated convolutions, poorly captures local fine structures and relies heavily on empirical tuning. To overcome these limitations, we propose an improved TCN-based SSP prediction method and apply it to seafloor geodetic positioning. The approach first constructs a sound speed increment field via first-order time differencing to remove global trends and highlight local variations. It then employs Optuna (version 4.9.0), a Bayesian sampling-based automatic optimization framework, to automatically tune key TCN parameters within a predefined search space, reducing reliance on manual tuning. The predicted high-resolution sound speed time series is finally used for ray tracing positioning to enhance seafloor geodetic accuracy. Experiments on the GLORYS12V1 reanalysis dataset show that LSTM and the original TCN achieve root mean square error (RMSE) and mean absolute error (MAE) values of 0.414 and 0.299 m/s, as well as 0.360 and 0.258 m/s, respectively, whereas our improved TCN reduces these to 0.205 and 0.131 m/s, substantially outperforming both baselines. In simulated Global Navigation Satellite System–Acoustics (GNSS-A) seafloor positioning, the 3D positioning RMSE drops to about 0.075 m, with improved stability. The proposed method offers an effective solution for accurate SSP time series forecasting and high-precision seafloor geodesy. Full article

Internal steel-core damage hidden within aluminum conductor steel-reinforced (ACSR) compression components may threaten the mechanical integrity of ultra-high-voltage transmission lines. This laboratory study evaluates metal magnetic memory testing (MMMT) responses to artificial discontinuities in seven-strand ACSR steel cores under four nominal lift-off distances, two nominal orthogonal specimen orientations, and a simplified aluminum-tube-covered condition. One intact specimen and five artificially damaged 1 m specimens were preloaded to 16 kN for 2 min, unloaded, and scanned using the normal magnetic-field component recorded by Channel 1 of a TSC-1M-4 detector. Quantitative descriptors included peak-to-peak amplitude, abnormal-field width, maximum gradient, and short-term within-specimen repeatability. At 5 mm lift-off, peak-to-peak amplitudes ranged from 18.7 to 91.4 A/m. Across three repeated repositioning scans, amplitude coefficients of variation ranged from 0.83% to 8.04%. Relative to 5 mm, the descriptive mean amplitude loss reached 66.3%, 81.9%, and 89.8% at 20, 30, and 40 mm, respectively. Orientation changed signal polarity and amplitude in a specimen-dependent manner. Anomalies remained visible under the aluminum-tube configuration, although covering and effective lift-off effects could not be separated. The results provide preliminary laboratory evidence for further evaluation of MMMT as a screening approach; the reported feature values are not field detection thresholds. Full article

Cryptosporidiosis is a significant zoonotic disease caused by Cryptosporidium species, primarily Cryptosporidium parvum, leading to serious economic losses in neonatal calves; the absence of an approved vaccine or fully effective treatment necessitates alternative therapeutic approaches. This study evaluated the therapeutic efficacy of albendazole (20 mg/kg, orally, 5 days) in naturally infected neonatal calves, comparing it to paromomycin (100 mg/kg, orally, 5 days) and an albendazole + paromomycin combination. Forty-eight Simmental calves (36 naturally infected, mean age of 14 days, and 12 healthy controls) were included. Infection was confirmed by rapid immunochromatographic tests, carbol fuchsin staining, nested PCR, and RFLP, identifying C. parvum as the causative agent. Fecal and blood samples were collected on days 0, 3, 7, 10, 20, and 30, alongside clinical evaluations and live weight measurements. Oocyst shedding declined significantly over the study period in all three treatment groups, with the paromomycin and albendazole + paromomycin groups showing a numerically earlier decline than the albendazole group; however, no statistically significant differences among the three treatment groups were detected at any individual sampling day. Fecal scores paralleled oocyst shedding patterns. No significant differences were detected among treatment groups in hematological, biochemical, or hepatorenal safety parameters. Albendazole demonstrated efficacy in reducing oocyst shedding in Cryptosporidium infection, suggesting it may serve as a viable alternative treatment option in field conditions. Full article

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which thereby fail to meet the high spatial resolution required for NWP. In this study, we present an integrated framework as an engineering refinement combining the variation method with an artificial neural network (Var-ANN) to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) aboard the polar-orbiting satellite FY-3C. The variation method is first applied to construct a spatially consistent reference field from available station observations, and this field is then used as the training target for a back-propagation neural network that learns the empirical relationship between satellite brightness temperatures and corrected atmospheric temperature. The calibrated temperature profiles were evaluated against independent radiosonde observations and further tested through assimilation into the Weather Research and Forecasting (WRF) model for precipitation simulation over the TP. Results indicate that the Var-ANN calibration reduces the root-mean-square error (RMSE) by approximately 60% and the mean bias from approximately −5 °C to −0.7 °C relative to radiosonde observations. In two WRF case studies, the calibrated profiles show potential for improving precipitation forecast skill, although the limited sample size precludes robust conclusions about operational forecast improvements. The Var-ANN framework provides a practical approach for enhancing the utility of FY-3C VASS temperature products for NWP applications over data-sparse complex terrain. Full article

Coastal power plants draw seawater from the open ocean through their cooling-water circulation systems. The cooling water falls over an overflow weir inside the siphon well, entraining large quantities of air, and generates a foam pollution plume upon discharge to the sea. By combining physical model experiments with numerical simulation, this study investigates the key factors governing foam stability and the aeration behavior of the water downstream of the siphon-well overflow weir. The principal conclusions are as follows: among the three single-factor variables tested in controlled laboratory conditions—temperature, salinity, and shellfish-flesh suspension concentration—the biological substance proxy showed the strongest effect on foam stability; when the shellfish-flesh suspension concentration reaches 20% (mass/volume basis, independently prepared), the foam volume and half-life increase by factors of 1.4 and 3.36, respectively, relative to the 4% baseline condition. When the dimensionless aeration depth z/z₉₀ < 0.75, the air-concentration profile rises relatively slowly with depth, whereas it increases more rapidly as the free surface is approached. Within the investigated viscosity range of 1.0–8.3 mPa·s (1.0 mPa·s for the pure-water control and 1.5–8.3 mPa·s for the measured viscosities of the 4–20% shellfish-flesh suspensions), the cross-sectional mean air concentration shows an overall decreasing trend as the liquid-phase viscosity increases, and the total bubble number density decreases correspondingly. The findings provide a laboratory-based indication of the mechanisms that must be addressed in the development of physical foam-suppression technologies; confirmation against field discharge water is required. Full article

Accurate wellbore-pressure prediction is essential for safe drilling and pressure management in ultra-deep wells, where high temperature and pressure strongly alter drilling-fluid rheology. Existing rheological-parameter models are often calibrated for specific fluids and narrow temperature–pressure ranges, limiting their use in ultra-deep-well hydraulics. We measured three water-based drilling fluids at temperatures and pressures up to 210 °C and 206.5 MPa, compared seven rheological models, and developed a multidimensional evaluation method considering global fitting accuracy, extreme-condition performance, low-shear-rate representation, absolute shear-stress deviation, and model complexity. Using ambient-condition Herschel–Bulkley (H-B) parameters as baselines, we proposed a temperature–pressure (T-P)-coupled correction model requiring fluid-specific calibration to predict H-B parameters over the tested range. The fluids exhibited temperature-induced thinning, pressure-induced thickening, and shear-thinning behavior. The H-B model showed the best overall performance, with mean R² values above 0.997 and mean absolute percentage errors below 2.5% for all fluids. Substituting the corrected parameters into the H-B equation yielded mean shear-stress errors no greater than 4.04%. Field validation showed that the T-P-coupled model reduced the mean circulating-pressure-loss error from 2.72% to 0.78%. This approach provides practical inputs for rheology estimation and circulating-pressure calculation in ultra-deep wells under wide temperature and pressure conditions. Full article

Triply periodic minimal surface (TPMS) heat exchangers offer high surface-area-to-volume ratios and interconnected flow pathways, making them attractive for compact thermal management. However, accurately predicting nanofluid heat transfer over a wide range of nanoparticle concentrations and operating conditions in complex TPMS geometries remains computationally challenging because of the coupled effects of porous architecture, flow dynamics, and concentration-dependent thermophysical properties. In this study, a hybrid physics-informed neural network (PINN) framework was developed to reconstruct concentration-dependent ${Al}_2{O}_3–water$ nanofluid temperature fields in TPMS gyroid heat exchangers. The originality of the proposed approach lies in integrating sparse thermocouple measurements, a steady-state convection–diffusion equation, boundary condition residuals, concentration-dependent nanofluid property models, and a physics-based concentration scaling procedure within a unified framework. The proposed framework was applied to aluminum and silver TPMS heat exchangers over a wide range of nanofluid volume fractions and flow conditions. The trained PINN accurately reconstructed the experimentally measured temperature field, demonstrating excellent agreement with the reference experimental data. Predictions at concentrations beyond the experimentally measured reference condition were obtained using the physics-based concentration scaling model. The effective heat transfer coefficient and Nusselt number were subsequently evaluated from the predicted mean TPMS temperature through an energy balance formulation. Increasing nanoparticle concentration reduced the predicted TPMS temperatures by approximately 17.5–18.5%, while the combined increase in concentration and flow rate produced an overall temperature reduction of about 33.5%. Relative to the selected baseline condition, the combined variation in concentration and flow rate was associated with calculated increases of 62.08% in $h_{eff}$, 58.33% in Nu, and 59.32% in Re. These results demonstrate the potential of the proposed hybrid PINN framework as a computationally efficient surrogate for evaluating nanofluid-enhanced TPMS heat exchangers, while acknowledging that predictions away from the training concentration depend on the validity of the concentration scaling model. Full article

Autonomous agricultural robots require accurate trajectory tracking to perform precision field operations such as seeding, fertilization, and pruning. Classical kinematic models fail to capture the nonlinear dynamics inherent to real field conditions, limiting the performance of model-based control strategies. This work proposes an optimal hybrid methodology integrating Takagi–Sugeno (T–S) fuzzy model identification and Pure Pursuit (PP) control within a Particle Swarm Optimization (PSO) framework for a simulated pruning tractor. Data-driven T–S fuzzy models for incremental displacements ${\mathcal{M}}_{\Delta x}$ and ${\mathcal{M}}_{\Delta y}$ are identified using Fuzzy C-Means and parameterized via PSO. These fuzzy models are embedded in a PP feedback control scheme with discrete-time PI velocity and PD steering controllers, whose four gains are tuned by a second PSO instance. The fuzzy models achieve identification Root-Mean-Square Errors (RMSEs) of 10.598 × 10⁻³ m and 8.125 × 10⁻³ m. Integrated into the control loop, the system yields a lateral RMSE of 6.6 × 10⁻³ m on the training path and generalizes effectively across twelve complex agricultural coverage trajectories, maintaining a lateral RMSE below 12 × 10⁻³ m and heading RMSE under 1 degree. This interpretable, fuzzy rule-based approach provides an accurate and replicable simulation baseline for future experimental implementation on physical platforms. Full article

Gradient-based inversion methods are widely used for time-domain airborne electromagnetic (AEM) data, but their results are commonly affected by the initial model and regularization-induced smoothing. These limitations are particularly evident when thin layers or alternating high- and low-resistivity structures need to be resolved. To address this problem, we propose a gradient-guided iterative enhancement (GGIE) inversion that combines a limited-iteration Gauss–Newton (GN) inversion with a lightweight U-Net. In each GGIE inversion iteration, the GN module first produces a coarse inverted model (IM) that preserves the main data-driven geoelectric trend but is still affected by regularization-induced smoothing. The trained U-Net then predicts a structurally enhanced model (PM) from the observed data and the IM. A data-misfit-guided adaptive approach is proposed to calculate the weight coefficients of the IM and PM and to construct an update model (UM). These coefficients are further smoothed by a momentum term so that the relative contributions of the IM and the PM are adjusted adaptively during the iterations. This design reduces error propagation from either component alone and dynamically balances learned structural enhancement with physics-based data consistency. The UM then serves as the initial model for the subsequent GN inversion. GGIE inversion is tested on synthetic data, and the results show that it is most beneficial for complex multilayer structures, for which it reduces the mean relative error and root mean squared error (RMSE) by 49.0% and 28.6%, respectively. Compared with the physics-informed neural network (PINN) baseline, GGIE inversion reduces the model relative error, log-domain RMSE, and data misfit by 19.4%, 7.0%, and 71.7%, respectively. Moreover, compared with U-Net alone, GGIE inversion reduces the data misfit by 85.4%. The proposed method is further applied to field data acquired from the Fox River area in Wisconsin, USA. The main advantage of GGIE inversion is its ability to resolve complex multilayered structures, thin layers, and sharp resistivity contrasts with improved accuracy and stability. Full article

In this study, the Boscovich curve of 1763 is reinterpreted as a mean-field potential for interacting particles in condensed matter. In a dense many-body system, each particle experiences an effective potential arising from the average distribution of all the others. This mean-field potential, which exhibits alternating maxima (energy barriers) and minima (coordination shells), thereby reducing the complexity of the N-body problem to an effective two-body radial problem, with the correlation distance r as the key variable. The relationship between the PMF and the radial distribution function g(r) is given by the Kirkwood equation U_B(r) = −kT ln g(r), which provides a multi-well potential in condensed matter. Furthermore, the system is described by the Fisher density functional equation for the correlation amplitudes, −2kT ∇²ψ(r) + U_B(r)ψ(r) = μψ(r) whose eigenvalues μ_i correspond to potential levels and whose eigenfunctions ψ_i are the correlation amplitudes of the coordination shell structure. Based on the multi-well potential picture, the oscillatory behavior of U_B(r) is modeled analytically by a weighted sum of Lennard-Jones potentials, modulated by sigmoid functions. The parameters—well depths, widths, and coordination distances—are assigned on the basis of known structural properties of the system, derived either from experimental data or from geometric models such as FCC or HCP lattices. The radial distribution function is then reconstructed as a linear combination of the squared eigenfunctions obtained from the Fisher equation. The resulting discrete eigenvalue spectrum provides a spectral interpretation of the shell structure of condensed matter, wherein the complexity of many-body interactions is encoded in a hierarchy of correlation modes, each associated with a specific coordination shell. Unlike classical DFT—which relies on approximate excess free-energy functionals—and Ornstein–Zernike theory—which requires closure approximations—our approach provides a direct spectral interpretation of the coordination shell structure through the eigenvalue spectrum of the Fisher equation, where the PMF acts as the effective potential and the radial distribution function is reconstructed as a combination of squared eigenfunctions. The method is validated for liquid argon and FCC lattices and establishes a historical connection with Boscovich’s curve as a statistical potential. Full article

Temperature–time profiles obtained through thermistor-based monitoring provide a rich but noise-sensitive source of information for estimating kinetic and thermal parameters in exothermic batch reactions. Conventional workflows typically combine deterministic smoothing with numerical differentiation, an approach that can amplify measurement noise and fail to propagate preprocessing uncertainty into the resulting reaction-rate and parameter estimates. To address these limitations, this study presents an uncertainty-aware Bayesian machine-learning framework that integrates scalable random-Fourier-feature Gaussian-process (RFF–GP) smoothing, analytical differentiation, temperature-derived apparent conversion, Bayesian parameter inference, posterior validation, predictive calibration, model comparison, ablation, sensitivity analysis, probabilistic benchmarking, simulation of thermal nonideality, and endpoint diagnostics. The framework was applied to $379,631$ cleaned thermistor observations. The production RFF–GP achieved a validation root-mean-square error of $0.04805\mathrm{K}$, yielding a stable latent temperature trajectory and an uncertainty-aware estimate of $dT/dt$. On a smaller matched subset, exact Gaussian-process regression achieved the highest predictive accuracy and the best probabilistic scores, whereas the RFF–GP reduced central-processing-unit runtime by approximately $4.1$-fold and remained applicable to the larger production fit. A Monte Carlo dropout neural comparator produced larger prediction errors and substantially wider predictive intervals. Six apparent thermokinetic structures were evaluated using mean-field variational inference, after which the nth-order and autocatalytic structures were validated using the No-U-Turn Sampler (NUTS). Under mean-field variational inference, the apparent autocatalytic structure achieved the lowest point estimate of the widely applicable information criterion (WAIC), the lowest derivative-domain error, and the lowest full-profile temperature-reconstruction root-mean-square error of $0.2920\mathrm{K}$. Its posterior obtained using NUTS yielded $E_a=40.98\mathrm{kJ}{\mathrm{mol}}^{-1}$, $k_{\mathrm{ref}}=0.005815{\min }^{-1}$, $\Delta T_{\mathrm{ad}}=56.11\mathrm{K}$, $m=0.1694$, and $n=1.0784$. The sampling diagnostics indicated satisfactory convergence, large effective sample sizes, and no divergent transitions. Although the MFVI posterior means and NUTS posterior medians were similar, variational inference produced narrower uncertainty intervals for several correlated parameters. Moving-block bootstrap intervals did not establish a decisive separation in WAIC among the leading structures. Expanded sensitivity, ablation, imperfect-insulation simulation, and endpoint-holdout analyses further showed that the apparent parameter estimates were sensitive to optimization, thermal nonideality, sensor response, and Gaussian-process boundary behavior. The autocatalytic formulation should therefore be interpreted as the best-performing apparent structure among the candidates tested rather than as evidence of a unique chemical mechanism. Overall, the framework extracted physically plausible apparent thermokinetic information from noisy temperature-only measurements while explicitly quantifying uncertainty arising from prediction, parameter estimation, model form, computation, thermal nonideality, and boundary behavior. Full article

Climate change is intensifying heat-related risks in Mediterranean urban environments, increasing the need for pedestrian-scale approaches that combine spatial diagnostics with lived thermal experience. The pilot study develops a three-layer diagnostic framework for assessing outdoor thermal comfort in the central neighbourhoods of Urla, İzmir, Türkiye. The framework integrates UMEP-SOLWEIG microclimatic modelling, citizen science field measurements and structured thermal perception diaries. First, Physiological Equivalent Temperature (PET) and mean radiant temperature (Tmrt) were modelled for 24 November 2023 and used as spatial diagnostic layers to identify three pilot areas with distinct urban morphologies. Second, an independent citizen science thermal walk campaign was conducted on 5 November 2025, during which 12 volunteers recorded in situ air temperature and relative humidity at predefined measurement points. Third, participants completed thermal diaries based on ASHRAE Standard 55 to document thermal sensation, comfort, preference, acceptability and adaptive responses. The modelling and fieldwork components were not designed as same-day validation datasets but as complementary layers for interpreting spatial thermal patterns and perceived thermal conditions. The results show that modelled PET values were relatively homogeneous across the three areas, while field-measured air temperature and subjective thermal responses varied more clearly at the pedestrian scale. These differences suggest that surface conditions, shading, spatial openness and user perception may jointly shape outdoor thermal experience, although the findings should be interpreted as descriptive tendencies due to the single autumn fieldwork session and small sample size. The study contributes a cautious, repeatable pilot framework for linking spatial screening, citizen-generated microclimatic data and structured perception evidence in climate-responsive urban design and local climate governance. Full article

Industrial heritage sites have predominantly been examined through visual, material, and conservation-oriented frameworks, while the haptic and embodied ways in which contemporary users experience reused industrial heritage environments remain comparatively underexplored. Focusing on Tophane-i Amire Culture and Art Centre, a historically significant Ottoman industrial heritage site repurposed as a contemporary cultural and artistic centre, this study examines how material contact, bodily movement, and tactile encounters contribute to the sensory heritage experience. Rather than proposing haptic sensewalking as a new method, the study uses an existing sensewalking approach in a haptic-centred way within the specific context of an Ottoman industrial heritage site undergoing adaptive reuse. Sensory data were generated through in situ walking observations, embodied sensory narratives, tactile descriptions, researcher field notes, and post-walk reflective accounts. The findings suggest that, within this haptic-focused protocol, participants did not describe the site merely as a visual-historical object. Instead, they articulated Tophane-i Amire as an embodied haptic environment in which rough stone textures, participant-perceived coldness and material heaviness, spatial thresholds, uneven walking surfaces, and movement-based encounters made architectural space bodily noticeable. The study does not claim that ordinary visitors naturally prioritise haptic experience; rather, it examines what participants articulated when their sensory attention was deliberately oriented toward tactile, thermal, kinaesthetic, and embodied aspects of the reused industrial heritage environment. The article makes two main contributions. Empirically, it shows how participants described haptic experience at Tophane-i Amire through encounters with rough stone surfaces, participant-perceived thermal qualities, thresholds, floors, and bodily movement. Conceptually, it interprets industrial heritage not only as a visual or material object but also as an embodied haptic environment in which historical meaning is sensed through touch, temperature, movement, and spatial negotiation. Full article